We want to analyze two RNA-Seq datasets derived from total RNA of fractionated Protrusions (Ps) and cell body (CB) fractions of mouse fibroblast cells. We are interested in RNAs that are enriched in protrusions (i.e. have a high Ps/CB ratio) and want to identify subsets of these RNAs that are co-regulated by different factors.
The first dataset aims at identifying RNAs whose enrichment at protrusions is (or is not) affected by knockdown of the APC tumor suppressor protein. We have sequenced 4 control (si-control) and 4 APC knockdown (si-APC) replicates. Each replicate consists of paired Ps and CB fractions (i.e. 16 samples total).
The second dataset aims at identifying RNAs whose enrichment at protrusions is affected by expression of a competing UTR construct, which mislocalizes RNAs through sequestration of necessary factors. We have sequenced 4 control (HBB) and 4 experimental (Pkp4) replicates. Each replicate consists of paired Ps and CB fractions (i.e. 16 samples total).
## Bioconductor version 3.2 (BiocInstaller 1.20.0), ?biocLite for help
#Command line version
module load subread
x=$(ls *.bam)
featureCounts -p -T 8 -s 2 -p -t exon -g gene_id -a /data/maggiec/RNASeq/Genomes/mm10/gencode.vM4.all.gtf -o counts_ss.txt $x
#Used R version:
gtf="/data/maggiec/RNASeq/Genomes/mm10/gencode.vM4.all.gtf"
targets <- readTargets()
fc <- featureCounts(files=targets$bam,isGTFAnnotationFile=TRUE,nthreads=32,
annot.ext=gtf,GTF.attrType="gene_name",strandSpecific=2,isPairedEnd=TRUE)
x <- DGEList(counts=fc$counts, genes=fc$annotation)
## [1] "fc" "gtf" "targets" "x"
## bam Cell Compartment Replicate Phenotype
## 1 Sample_HBB_CB_1.bam HBB CB 1 HBB_CB
## 2 Sample_HBB_CB_2.bam HBB CB 2 HBB_CB
## 3 Sample_HBB_CB_3.bam HBB CB 3 HBB_CB
## 4 Sample_HBB_CB_4.bam HBB CB 4 HBB_CB
## 5 Sample_HBB_Ps_1.bam HBB Ps 1 HBB_Ps
## 6 Sample_HBB_Ps_2.bam HBB Ps 2 HBB_Ps
## 7 Sample_HBB_Ps_3.bam HBB Ps 3 HBB_Ps
## 8 Sample_HBB_Ps_4.bam HBB Ps 4 HBB_Ps
## 9 Sample_Pkp4_CB_1.bam Pkp4 CB 1 Pkp4_CB
## 10 Sample_Pkp4_CB_2.bam Pkp4 CB 2 Pkp4_CB
## 11 Sample_Pkp4_CB_3.bam Pkp4 CB 3 Pkp4_CB
## 12 Sample_Pkp4_CB_4.bam Pkp4 CB 4 Pkp4_CB
## 13 Sample_Pkp4_Ps_1.bam Pkp4 Ps 1 Pkp4_Ps
## 14 Sample_Pkp4_Ps_2.bam Pkp4 Ps 2 Pkp4_Ps
## 15 Sample_Pkp4_Ps_3.bam Pkp4 Ps 3 Pkp4_Ps
## 16 Sample_Pkp4_Ps_4.bam Pkp4 Ps 4 Pkp4_Ps
## Status Sample_HBB_CB_1.bam Sample_HBB_CB_2.bam
## 1 Assigned 28219262 26002638
## 2 Unassigned_Ambiguity 440881 397555
## 3 Unassigned_MultiMapping 5851372 5156921
## 4 Unassigned_NoFeatures 1256514 1126695
## 5 Unassigned_Unmapped 0 0
## 6 Unassigned_MappingQuality 0 0
## 7 Unassigned_FragementLength 0 0
## 8 Unassigned_Chimera 0 0
## 9 Unassigned_Secondary 0 0
## 10 Unassigned_Nonjunction 0 0
## 11 Unassigned_Duplicate 0 0
## Sample_HBB_CB_3.bam Sample_HBB_CB_4.bam Sample_HBB_Ps_1.bam
## 1 31276043 20215388 24174225
## 2 498844 336763 484750
## 3 6530007 4027521 8503721
## 4 1409443 802542 669031
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## Sample_HBB_Ps_2.bam Sample_HBB_Ps_3.bam Sample_HBB_Ps_4.bam
## 1 21033948 21092274 21707668
## 2 409691 436089 417646
## 3 7014376 8130277 6366255
## 4 640754 529615 574098
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## Sample_Pkp4_CB_1.bam Sample_Pkp4_CB_2.bam Sample_Pkp4_CB_3.bam
## 1 27137705 24478990 26951417
## 2 458180 380978 426721
## 3 6335410 5034420 5581745
## 4 996922 924351 1112709
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## Sample_Pkp4_CB_4.bam Sample_Pkp4_Ps_1.bam Sample_Pkp4_Ps_2.bam
## 1 21872625 25005064 18667911
## 2 390419 499736 387524
## 3 5023467 9049572 6576287
## 4 754598 597725 551206
## 5 0 0 0
## 6 0 0 0
## 7 0 0 0
## 8 0 0 0
## 9 0 0 0
## 10 0 0 0
## 11 0 0 0
## Sample_Pkp4_Ps_3.bam Sample_Pkp4_Ps_4.bam
## 1 23950416 20046535
## 2 551285 386252
## 3 8631717 5899079
## 4 548449 513661
## 5 0 0
## 6 0 0
## 7 0 0
## 8 0 0
## 9 0 0
## 10 0 0
## 11 0 0
## [1] 12900 16
## null device
## 1
## Using as id variables
## null device
## 1
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## celltypeHBB_CB celltypeHBB_Ps celltypePkp4_CB celltypePkp4_Ps
## 1 1 0 0 0
## 2 1 0 0 0
## 3 1 0 0 0
## 4 1 0 0 0
## 5 0 1 0 0
## 6 0 1 0 0
## 7 0 1 0 0
## 8 0 1 0 0
## 9 0 0 1 0
## 10 0 0 1 0
## 11 0 0 1 0
## 12 0 0 1 0
## 13 0 0 0 1
## 14 0 0 0 1
## 15 0 0 0 1
## 16 0 0 0 1
## attr(,"assign")
## [1] 1 1 1 1
## attr(,"contrasts")
## attr(,"contrasts")$celltype
## [1] "contr.treatment"
## [1] "HBB_CB" "HBB_Ps" "Pkp4_CB" "Pkp4_Ps"
## R version 3.2.1 (2015-06-18)
## Platform: x86_64-apple-darwin13.4.0 (64-bit)
## Running under: OS X 10.10.4 (Yosemite)
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] reshape2_1.4.1 d3heatmap_0.6.1 reshape_0.8.5
## [4] knitr_1.11 rgl_0.95.1367 ggplot2_1.0.1
## [7] edgeR_3.12.0 limma_3.26.1 Rsubread_1.20.1
## [10] BiocInstaller_1.20.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_0.12.1 magrittr_1.5 MASS_7.3-44
## [4] munsell_0.4.2 colorspace_1.2-6 stringr_1.0.0
## [7] plyr_1.8.3 tools_3.2.1 DT_0.1
## [10] grid_3.2.1 gtable_0.1.2 png_0.1-7
## [13] htmltools_0.2.6 yaml_2.1.13 digest_0.6.8
## [16] RColorBrewer_1.1-2 formatR_1.2.1 base64enc_0.1-3
## [19] htmlwidgets_0.5 evaluate_0.8 rmarkdown_0.8.1
## [22] labeling_0.3 stringi_0.5-5 scales_0.3.0
## [25] jsonlite_0.9.17 proto_0.3-10